Anonymous patient data may not be as private as previously thought

“The study shows that machine learning can successfully re-identify the de-identified physical activity data of a large percentage of individuals, and this indicates that our current practices for de-identifying physical activity data are insufficient for privacy,” said study coauthor Anil Aswani of the University of California, Berkeley. “More broadly it suggests that other types of health data that have been thought to be non-identifying could potentially be matched to individuals by using machine learning and other artificial intelligence technologies.”